Overview

This file presents a brief EDA shows a set of initial plots for Chicago gang boundary analysis.

Datasets

Data for gang boundaries are obtained from the Police Department GIS Data as part of the Chicago Police Department CLEARMap database, provided by the Office of Public Safety Administration (OPSA). The data are updated annually. Availabble in the dataset are the data from 2007 to 2021, except for 2013 and 2020.


Map of Chicago Community Boundaries

The gang boundaries are overlaid with Chicago community boundaries shown below.

library(sf)
library(ggplot2)
library(tidyverse)
library(gridExtra)
library(pointdexter)

chicago_community <- st_read(
  'data/Boundaries - Community Areas (current)/geo_export_2099592d-f4d8-46cf-9881-dd554256e0fd.shp'
)
ggplot() + 
  geom_sf(data = chicago_community) + 
  ggtitle("Chicago Boundary Plot (Community)") + 
  coord_sf() +
  theme_minimal()


Chicago gangs and alliances

In total, there are 50 gangs in Chicago as of 2021. They can majorly be divided into two major alliances.

The Folk Nation (commonly referred to as Folk or Folks) is an alliance of street gangs originating in Chicago, established in 1978. The alliance has since spread throughout the United States, particularly the Midwest region of the United States. They are rivals of the People Nation.
People Nation is an alliance of street gangs generally associated with the Chicago area. They are rivals of the Folk Nation alliance of gangs. The People Nation was also formed in 1978 in reaction to the creation of the Folk Nation alliance of gangs.
Other than the people and folks nation, some gangs choose not to join either of the alliance.
Shown below is a distribution of the gangs belong to each alliance.

# explore nations
nations_2021 <- read_csv('data/2021_Gang_Boundaries_and_nations.csv')
nations_2021 %>% 
  mutate(nation = factor(nation, levels = c('People','Folks','None'))) %>% 
  ggplot()+
  geom_bar(aes(x = nation,
               color = nation,
               fill = nation),
           alpha = 0.4)+
  ggtitle('Alliance of Chicago Gangs -- 2021') + 
  theme_minimal()

# read in all gang nations
nations <- read_csv('data/Gang_nations.csv') %>% 
  mutate(nation = factor(nation, levels = c('People','Folks','None')))

In our dataset, there are 19 gangs in the people nation, 26 gangs in the folks nation, and 5 gangs choose not to join either of the alliance.

Overlay Gang Boundary with Community Boundary in Chicago

# community boundaries
data("community_areas_spdf")
# create list of coordinate pair matrices for each community area ----
community.area.boundaries <-
  GetPolygonBoundaries(my.polygon = community_areas_spdf
                       , labels = community_areas_spdf$community)

# turn off the s2 processing
sf::sf_use_s2(FALSE)

gang_boundary_2021 <- 
  st_read(
    "data/2021_Gang_Boundaries/2021_Gang_Boundaries.shp") %>% 
  left_join(nations %>% 
              dplyr::select(GANG_NAME, nation) %>% 
              mutate(nation = factor(nation, levels = c('People','Folks','None'))), 
            by = 'GANG_NAME') %>% 
  st_cast("POLYGON") %>% 
  st_transform(4326) %>% 
  mutate(
    # find the centroid of each gang territory
    centroid = st_centroid(.)$geometry,
    # get the latitude and longitude for centroids
    centroid_lng = st_coordinates(centroid)[,1],
    centroid_lat = st_coordinates(centroid)[,2],
    # find the corresponding community
    community = LabelPointsWithinPolygons(lat = centroid_lat,
                                          lng = centroid_lng,
                                          polygon.boundaries = community.area.boundaries))

ggplot() + 
  geom_sf(data = chicago_community,
          color = 'gray') + 
  geom_sf(data = gang_boundary_2021, 
          aes(fill = nation,
              color = nation),
          alpha = 0.8) + 
  geom_sf(data = gang_boundary_2021$centroid, 
          color = 'pink',
          alpha = 0.8,
          size = 0.5) + 
  coord_sf() +
  ggtitle('Gang Boundaries 2021 (with centroid location)') +
  theme_minimal()

From the map, we can see folks nation gangs have higher coverage in terms of area of territories. However, the distribution of people nation and folks nation gangs are fairly even through out the city. There are more people nation gangs in the west, while there are more folks nation gangs in the south. Note that each gang may have more than one territory, so the number of territories shown on the map should not equal to the number of gangs existing in Chicago.

Changes over 2007 to 2021

First we investigate the changes in the number of gangs during 2007 and 2021.

gang_bound <- function(yr){
  gang_boundary <- st_read(
    paste0("data/", yr, "_Gang_Boundaries/", yr, "_Gang_Boundaries.shp")) %>% 
    mutate(year = yr)
  # merge with 
  gang_boundary <- gang_boundary %>% 
    left_join(nations %>% 
                dplyr::select(GANG_NAME, nation) %>% 
                mutate(nation = factor(nation, levels = c('People','Folks','None'))), 
              by = 'GANG_NAME') %>% 
    st_cast("POLYGON") %>% 
    st_transform(4326) %>% 
    mutate(
      # find the centroid of each gang territory
      centroid = st_centroid(.)$geometry,
      # get the latitude and longitude for centroids
      centroid_lng = st_coordinates(centroid)[,1],
      centroid_lat = st_coordinates(centroid)[,2],
      # find the corresponding community
      community = LabelPointsWithinPolygons(lat = centroid_lat,
                                            lng = centroid_lng,
                                            polygon.boundaries = community.area.boundaries))
  names(gang_boundary) <- c("OBJECTID", "GANG_NAME", "Shape__Are", "Shape__Len", "year", 'nation', "geometry","centroid",
                            "centroid_lng", "centroid_lat", "community")
  return(gang_boundary)
}

gang_boundary_2007_2019 <- gang_boundary_2021 %>% 
  mutate(year = 2021) %>% 
  dplyr::select(OBJECTID, GANG_NAME, Shape__Are, Shape__Len, year, nation, geometry, centroid, centroid_lng, centroid_lat, community)
for (x in c('2007','2008','2009','2010','2011','2012','2014','2015','2016','2017','2018','2019')) {
  gang_boundary_2007_2019 <- rbind(gang_boundary_2007_2019, gang_bound(x))
}


gang_boundary_2007_2019 %>% 
  as_tibble() %>% 
  group_by(year) %>% 
  summarise(count = n()) %>% 
  ggplot(aes(x = year, y = count, group = 1)) +
  geom_line() +
  stat_smooth()+
  geom_point() +
  labs(
    title = 'Number of Gangs Territories in Chicago during 2007 and 2021',
    subtitle = '(Note: data for 2013 and 2020 are missing)'
  ) +
  theme_minimal()

By visualizing the changes in number of gang territories, we see a decreasing trend in Chicago. It’ll be interest

Next, we will visualize the change over the 2007 to 2020 (since data for 2021 are already shown above).

ggplot() + 
  geom_sf(data = chicago_community,
          color = 'gray') + 
  geom_sf(data = gang_boundary_2007_2019 %>% filter(year<2021), 
          aes(fill = nation,
              color = nation),
          alpha = 0.8) + 
  facet_wrap(~ year, ncol = 3) +
  coord_sf() +
  theme_minimal() +
  theme(text = element_text(size = 20))

From the facet figure, we observe majority of People nation gangs distribute in the west of Chicago, whereas Folks nation gangs distribute in the south of Chicago. Those are the regions where more of the low income families reside.

Number of gang territories in each commnunity

years=c(2007,2008,2009,2010,2011,2012,2014,2015,2016,2017,2018,2019,2021)

gang_boundary_2007_2019 %>% 
  as_tibble() %>% 
  dplyr::select(GANG_NAME, year, community) %>% 
  group_by(community, year) %>% 
  summarise(n_gang_territories = n(),
            year = max(year)) %>% 
  right_join(bind_rows(replicate(13, chicago_community %>% 
                                  as_tibble() %>% 
                                  dplyr::select(community), simplify = FALSE)) %>% 
               mutate(year = as.character(rep(years,each = 77))), 
             by = c('community', 'year')) %>% 
  left_join(chicago_community, by = 'community') %>%
  # mutate(n_gang_territories = replace_na(n_gang_territories, 0)) %>% 
  st_as_sf() %>% 
  ggplot() +
  geom_sf(aes(fill = n_gang_territories),
          alpha = 0.8) +
  facet_wrap(~ year, ncol = 3) +
  coord_sf() +
  theme_minimal() +
  scale_fill_gradient(low="#77dd76", high="#ff6962",
                      name = "# of Gang \nTerritories")+
  theme(text = element_text(size = 20))

Since many of the gang territories are across the border of the communities, we find the centroids for each community. The number of gang territories in a community is counted as number of centroids in the area. Please note that if a gang has mutliple territories, which is fairly common, then it will be count multiple times. Although there are gang territories in the south side, those territories are speadout rather than sharing a single community. In this sense, we may expect less conflicts among gangs when they are less concentrate.

The community with the largest number of gang territories is Austin over the years. Austin has been recognized as the dealiest neighborhood in Chicago according to news.

On the contrary, communities of Forest Glen, Norwood Park, Edison Park, Loop, Mount Greenwood never had gang territories over the years.

Divvy application (2021)

## public data mapped from ‘/projects/e30686/data’
divvy2021 <- read_csv('public_data/data/raw/divvy_2021.csv')

# number of actions on each station
divvy2021_frequency <- divvy2021 %>% 
  group_by(id) %>% 
  summarise(action = n(),
            station_name = max(station_name),
            longitude = max(longitude),
            latitude = max(latitude),
            location = max(location)) %>% 
  # filter out Evanston
  filter((longitude != 0) & (latitude != 0) & (latitude <= 42.01942
))

# map on 2021 gang data
gang_boundary_2007_2019 %>% 
  as_tibble() %>% 
  dplyr::select(GANG_NAME, year, community) %>% 
  group_by(community, year) %>% 
  summarise(n_gang_territories = n(),
            year = max(year)) %>% 
  right_join(bind_rows(replicate(13, chicago_community %>% 
                                   as_tibble() %>% 
                                   dplyr::select(community), simplify = FALSE)) %>% 
               mutate(year = as.character(rep(years,each = 77))), 
             by = c('community', 'year')) %>% 
  left_join(chicago_community, by = 'community') %>%
  filter(year == '2021') %>%
  st_as_sf() %>% 
  ggplot() +
  geom_sf(aes(fill = n_gang_territories),
          alpha = 0.8) +
  geom_point(data = divvy2021_frequency,
          aes(x = longitude,
              y = latitude,
              size = action),
          color = '#8281a0',
          alpha = 0.4) +
  scale_size(range = c(0, 20)) +
  coord_sf(xlim = c(-88.0, -87.5), 
           ylim = c(41.60, 42.05)) +
  coord_sf() +
  theme_minimal() +
  scale_fill_gradient(low="#77dd76", high="#ff6962",
                      name = "# of Gang \nTerritories") +
  labs(size='# of actions') +
  theme(text = element_text(size = 20))

The number of action is the number of transction happened at a given divvy station in 2021, i.e. the number of time stamps in the divvy2021.csv dataset. From the plot we see high density of activities are by the lake and in the north, with Loop, Lincoln Park, Lakeview, Near North Side having the most dense activities. Those communities are also have less number of gang.

Looking at the community with the highest number of gang territories, there are still some activities in the Divvy bike rent and return, be much less dense when compare with east side of the city.